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Indicadores para monitoramento de pesquisa em saúde no Brasil

2006· article· pt· W2135531615 on OpenAlexaff
Flávia Silva Elias, Luis Agnaldo Pereira De Souza

Bibliographic record

VenueCiência da Informação · 2006
Typearticle
Languagept
FieldSocial Sciences
TopicPublic Health in Brazil
Canadian institutionsUniversité de Montréal
FundersMinistério da Saúde
KeywordsPolitical scienceHumanitiesGynecologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

No Brasil, o Ministério da Saúde é importante financiador e usuário de pesquisas e projetos de desenvolvimento tecnológico. No entanto, os mecanismos formais de acompanhamento do fomento a pesquisas são incipientes. O objetivo do trabalho foi propor indicadores para monitorar o fomento das pesquisas financiadas. As necessidades de informação foram identificadas, e os indicadores foram formulados baseados em visitas a órgãos de fomento à pesquisa no Brasil, revisão de literatura e reuniões técnicas. Os indicadores informam quais pesquisas são financiadas e qual a correlação com as prioridades da política de saúde; como ocorre a distribuição do financiamento; quais externalidades foram produzidas pela pesquisa. Discute-se a importância do monitoramento para designar recursos em pesquisas prioritárias. O uso dos indicadores pode guiar a construção de base de dados no Ministério da Saúde.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.351
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2006
Admission routes1
Has abstractyes

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